CS5300 Database Systems

Transcription

1 CS5300 Database Systems Relational Algebra A.R. Hurson 323 CS Building

2 This module is intended to introduce: relational algebra as the backbone of relational model, and set of operations defined within the scope of relational model. 2

3 Note, this unit will be covered in two lectures. In case you finish it earlier, then you have the following options: 1) Take the early test and start CS5300.module3 2) Study the supplement module (supplement CS5300.module2) 3) Act as a helper to help other students in studying CS5300.module2 Note, options 2 and 3 have extra credits as noted in course outline. 3

4 Enforcement of background Current Module { Glossary of prerequisite topics No Familiar with the topics? Yes Take Test No Pass? Remedial action Yes { Glossary of topics No Familiar with the topics? Take the Module Yes Take Test No Pass? Yes Options Study next module? Review CS5300.module2.background Lead a group of students in this module (extra credits)? Study more advanced related topics (extra credits)? At the end: take exam, record the score, impose remedial action if not successful 4 Extra Curricular activities

5 You are expected to be familiar with: Relational data model Basic operations in relational model If not, you need to study CS5300.module2.background 5

6 Relational Algebra Relational model is based on set theory. relation is simply a set. The relational algebra is a set of high level operators that operate on relations. Each relational operator assumes one or two relations as input (unary or binary operators) and produces a relation as output. Relational algebra is composed of eight operators divided into two groups. A 6

7 Relational Algebra The traditional set operations: UNION, INTERSECTION, DIFFERENCE, and CARTESIAN PRODUCT. The special relational operations: RESTRICT, PROJECT, JOIN, and DIVIDE. 7

8 Relational Algebra Two relations are called Union-compatible if and only if they have identical headings, more precisely; They have the same set of attributes, and Corresponding attributes are defined on the same domain. Union, Intersection, and Difference require their operands to be union-compatible. 8

9 Set Operation Union: The union of two union-compatible relations A and B is a relation with the same heading as A and B (same schema) and a body consisting of the set of all tuples t belonging to either A or B (or both). A B = {t t A or t B} 9

10 Set Operation Intersection: Intersection of two unioncompatible relations A and B is a relation with the same heading as each of A and B (same schema) and with a body consisting of the set of the tuples t belonging to both A and B. A B = {t t A and t B} 10

11 Set Operation Difference: Difference between two unioncompatible relations A and B is a relation with the same heading as A and B (same schema) and with a body consisting of the set of tuples belonging to A and not to B. A B = {t t A and t B} 11

12 Set Operation Cartesian Product: Cartesian product of two product-compatible (disjoint headings) relations A and B, is a relation with a heading that is the coalescing of the headings of A and B (concatenated schemas) and a body consisting of the set of tuples t such that t is the concatenation of a tuple a from A and a tuple b from B. A Χ B = {<a,b> a A and b B} 12

13 Assume the following two relations: A S# SNAME STATUS CITY S1 Smith 20 London S4 Clark 20 London B S# SNAME STATUS CITY S1 Smith 20 London S2 Jones 10 Paris 13

14 A B = S# SNAME STATUS CITY S1 Smith 20 London S4 Clark 20 London S2 Jones 10 Paris A B = S# SNAME STATUS CITY S1 Smith 20 London A B = S# SNAME STATUS CITY S4 Clark 20 London 14

15 Set Operation Union, Intersect, and Product are associative operations; Union, Intersect, and Product are commutative operations; however, A (B C) (A B) C A B B A A B B A 15

16 Special Relational Operations Restriction (Select): Let θ {=, <, >,,...}, the θ-restriction of relation A on attributes X and Y is a relation with the same heading as A and a body consisting of the set of all tuples t in A that satisfy X θ Y. σ X θ Y (A) = {t t A and (t x θ t y is true)} θ-restriction effectively yields a horizontal subset of a given relation. 16

17 S S# Sname Status City S 1 Smith 20 London S 2 Jones 10 Paris S 3 Blake 30 Paris S 4 Clark 20 London S 5 Adams 30 Athens P P# Pname Color Weight City P 1 Nut Red 12 London P 2 Bolt Green 17 Paris P 3 Screw Blue 17 Rome P 4 Screw Red 14 London P 5 Cam Blue 12 Paris P 6 Cog Red 19 London 17

18 σ city= London (S) = S# Sname Status City S 1 Smith 20 London S 4 Clark 20 London σ Weight < 14 (P) = P# Pname Color Weight City P 1 Nut Red 12 London P 5 Cam Blue 12 Paris 18

19 Special Relational Operations Projection: Projection of a relation A on attributes X, Y,., is a relation with the heading of (X, Y, ) and a body consisting of all tuples (X:x, Y:y, ) such that a tuple t in A with the same X-value x, Y-value y, Π (X,Y, ) (A) = {r r x = t x, r y = t y, and t A } Projection operator effectively yields vertical slice of a given relation. 19

20 Π (City) (S) = City London Paris Athens Π (Color, City) (P) = Color City Red London Green Paris Blue Rome Blue Paris 20

21 Special Relational Operations Join (restrictive Cartesian product): Join operation comes in several variations: Natural Join θ-join Outer Join A c B = σ c (A Χ B) 21

22 Natural Join: Natural join of two relations A and B is a relation consisting of tuples t = <a, b> such that a is a tuple of A and b is a tuple of B and the common attribute values of A and B are equal Equijoin on all common fields. A c B = { <a,b> a A, b B, and a c =b c } Natural join is both associative and commutative. 22

23 θ-join: Let relations A and B be productcompatible (no attribute names in common) and let θ {<, >,,...}, then the θ-join of relation A on attribute X with relation B on attribute Y is a relation consist of tuples t from Cartesian product of A and B such that t x θ t y is true. A θ B = { <a,b> <a,b> A Χ B, and a X θ b Y is true} 23

24 A S.City=P.City B S# Sname Status S.City P# Pname Color Weight P.City S 1 Smith 20 London P 1 Nut Red 12 London S 1 Smith 20 London P 4 Screw Red 14 London S 1 Smith 20 London P 6 Cog Red 19 London S 2 Jones 10 Paris P 2 Bolt Green 17 Paris S 2 Jones 10 Paris P 5 Cam Blue 12 Paris S 3 Blake 30 Paris P 2 Bolt Green 17 Paris S 3 Blake 30 Paris P 5 Cam Blue 12 Paris S 4 Clark 20 London P 1 Nut Red 12 London S 4 Clark 20 London P 4 Screw Red 14 London S 4 Clark 20 London P 6 Cog Red 19 London 24

25 Division: Let A and B be two relations where set of B attributes are included in the one of A A (X 1, X 2,., X m, Y 1, Y 2,, Y n ) and B (Y 1, Y 2,, Y n ), then A B is a relation with the heading (X) and a body of the set of all tuples (X;x) such that (X:x, Y:y) is in A for all tuples (Y:y) in B. A B = { <a (X)> <a,b> A, <b> B} 25

26 Consider the following relations: A S# P# S1 S1 S1 S1 S1 S1 S2 S2 S3 S4 S4 S4 P1 P2 P3 P4 P5 P6 P1 P2 P2 P2 P4 P5 B B P# P1 P# P2 P4 B P# P1 P2 P3 P4 P5 P6 26

27 A B = S# S1 S2 A B = S# S1 S4 A B = S# S1 A S# P# S1 S1 S1 S1 S1 S1 S2 S2 S3 S4 S4 S4 P1 P2 P3 P4 P5 P6 P1 P2 P2 P2 P4 P5 B B B P# P1 P# P2 P4 P# P1 P2 P3 P4 P5 P6 27

28 Missing Information The problem of missing or unknown information is a very important data base issue. Information is very often incomplete. So we need a way of defining such incompleteness and a way to manipulate incomplete data bases. Discussion of the use of the Universal relation assumption has also increased interest in dealing with null values. A special column value called null ( ) is used to represent incomplete data. 28

29 Missing Information A partial tuple is one that contains one or more null values ( ). A tuple t is said to be total (t ) if and only if it contains no null values. This definition can be extended to relations as well. A relation A is total (A ) if all of its tuples are total. Relational operations using a search condition to select tuples will be basically affected by the incomplete data. 29

30 Missing Information Codd proposed the concept of three-valued logic as the basis to manipulate null values. Three-valued logic NOT T ω F F ω T AND T ω F T T ω F ω ω ω F F F F F OR T ω F T T T T ω T ω ω F T ω F 30

31 Maybe Algebra A truth-valued (logic) expression has the value of ω if and only if: Each occurrence of a null value in the expression can be replaced by a non-null value so as to yield the truth value T for the expression. Each occurrence of a null value in the expression can be replaced by a non-null value so as to yield the truth value F for the expression. 31

32 Maybe Algebra Assume the following two relations r(r) A B C r a 1 b 1 1 a 2 b 2 a 3 b 3 2 s(s) C s D 1 d 1 d 2 32

33 True Select r when C=1: A B C r a 1 b 1 1 r(r) Maybe Select r when C=1: A B C r a 1 b 1 1 a 2 b 2 a 3 b 3 2 A B C r a 2 b 2 33

34 True Join r and s over C: A B C r C s D a 1 b d 1 Maybe Join r and s over C: A B C r C s D a 1 b 1 1 d 2 a 2 b 2 1 d 1 a 2 b 2 d 2 a 3 b 3 2 d 2 r(r) s(s) A B C r a 1 b 1 1 a 2 b 2 a 3 b 3 2 C s D 1 d 1 d 2 34

35 Maybe Division: relations: Assume the following two r(r) A B C a 1 b 1 c 1 a 2 b 1 c 1 a 1 b 2 c 2 a 2 b 2 s(s) B C b 1 c 1 b 2 c 2 35

36 True Division (r s): A a 1 Maybe Division (r s): A a 2 r(r) s(s) A B C a 1 b 1 c 1 a 2 b 1 c 1 a 1 b 2 c 2 a 2 b 2 B C b 1 c 1 b 2 c 2 36

37 A somewhat different views of the null value is taken for the remaining relational algebra operations. For these operations, null values in different tuples are interpreted as being equivalent. Therefore, there is no changing in these operations over their use in traditional relational algebra. r(r) A B C Π (A,B) r A B a 1 c 1 a 1 a 1 c 2 a 2 b 2 a 2 b 2 a a 3 b 3 c 3 b

38 Missing Information Concept of null values can be used to bring the so-called dangling tuples into the join process and make relations union-compatible. 38

39 Outer Join θ : This is a direct consequence of dealing with incompleteness. In outer join, tuples in the join relations that do not match the join condition, will be extended by nulls and appear in the resultant relation. There are several variation of outer join: Left outer join right outer join full outer join 39

40 Outer Join r(a,b) and s(c,d) over B θ C (B and C are defined over the same domain and θ is the test condition) is defined as follows: r θ s = T (R 1 Χ (C :, D : )) ((A :, B : ) Χ S 1 ) Where: T is the true Join of r and s over B θ C, R 1 = r Π (A,B) (T) S 1 = s Π (C,D) (T) 40

41 Outer Join: An example, assume θ is equality, and r A B a 1 b 1 a 2 b 2 a 3 b 3 s C D b 1 d 1 b 2 d 2 b 4 d 3 41

42 r θ s = T (R 1 Χ (C :, D : )) ((A :, B : ) Χ S 1 ) R 1 = r Π (A,B) (T) S 1 = s Π (C,D) (T) T R 1 S 1 A B C D a 1 b 1 b 1 d 1 a 2 b 2 b 2 d 2 A B a 3 b 3 C D b 4 d 3 r θ s A B C D a 1 b 1 b 1 d 1 a 2 b 2 b 2 d 2 a 3 b 3 b 4 d 3 42

43 Assume the following relations: Sailors(sid:integer, sname:string, rating:integer, age:real) Boats(bid:integer, bname:string, color:string) Reserves(sid:integer, bid:integer, day:date) 43

44 1 04 Database Systems Sailors sid sname rating age 22 Dustin Burton Lubber Andy Rusty Horatio Zorba Horatio Art Bob Boats bid bname color 101 Interlake Blue 102 Interlake Red 103 Clipper Green 104 Marine red 44

45 Reserved sid bid day /10/ /10/ /8/ /7/ /10/ /6/ /12/ /5/ /8/ /8/98 45

46 Find names of sailors who have reserved a red boat Π sname (( σ color= red Boats) Reserves Sailors) sname Dustin Lubber Horatio 46

47 A more efficient solution would do the projections as early as possible: Π sname (Π sid ( (Π bid (σ color= red Boats) ) Reserves) Sailors) 47

48 Questions Find sailors that have reserved a green boat and a red boat. Identify sailors reserving red boats Identify sailors reserving green boats Intersect the sets and determine the sailors name What are the names of the sailors with the lowest rating (whatever that rating is). Compare the rating of each sailor with that of every other sailor Result is the set of sailors that never come out on top in one of these comparisons. 48